1. Field
The following description relates to a city street search service, and more particularly, to a server and method for providing a search service using a feature extracted from a city street image.
2. Description of Related Art
Generally, a city street search service, which provides information regarding a region or a building about which a user has questions, utilizes wireless positioning technology using Global Positioning System (GPS) and WiFi. In another way, a search can be performed using an image feature. For a city street search using an image, advantageously, a target of which information is desired by a user can be directly photographed and found.
For an image-based search service, a city-scale, massive image database (DB) needs to be built, and a search technology dealing with obstacles such as trees, roads, or vehicles in an urban environment is needed.
In the paper “City-scale landmark identification on mobile devices,” IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June 2011, presented by D. Chen, G. Baatz, K. Koeser, S. Tsai, R. Vedantham, T. Pylvanainen, K. Roimela, X. Chen, J. Bach, M. Pollefeys, B. Girod, and R. Grzeszczuk, a service DB for street search has been built by correcting image distortion on the basis of a captured panorama image in addition to three-dimensional (3D) information. In this process, a 3D model is essential, and expensive equipment for obtaining depth information, such as Light Detection and Ranging (LIDAR), is needed. One drawback of such an approach is that it cannot be applied to a search image of a user who utilizes a camera of a terminal. In addition, the approach does not consider obstacles such as trees, roads, vehicles, etc.
In order to solve such a problem of the obstacles, the paper “Avoiding confusing features in place recognition”, In ECCV, Chersonissos, 2010, presented by J. Knopp, J. Sivic and T. Pajdla has proposed a method of selecting an area such as a tree from an image by utilizing a matching relation between features of a search image and a distant image on the basis that a peripheral image is visually different from a distant image. However, this method has limitations in that it takes too much calculation time to perform comparisons with average M distant images when total N pieces of data for building a DB have been obtained. In addition, when there is no location information of the search image, it is difficult to obtain distant images.
U.S. Pat. No. 8,532,400 B1, entitled “Scene Classification For Place Recognition,” discloses a process of utilizing camera sensor information primarily in order to search for a place and then determining whether a captured image is an image of a place or not by utilizing image processing and meta information. This process includes searching for an image determined as being an image of a place. However, the solution of building a large-scale DB and the method of extracting and matching features while considering aforementioned obstacle areas are not disclosed.